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Correlation vs. Causation in Experimentation and Data Analysis
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The 95% Confidence Interval’s Impact on Experimentation Results
TL;DR:
- 95% confidence interval provides a range for experiment results, with 95% certainty the true value falls within that range
- It became standard due to its balance between precision and reliability, pioneered by statistician Sir Ronald Fisher
- Key influencing factors include sample size (larger = narrower interval), data variance, and chosen confidence level
- For decision-making: positive intervals (e.g., 1% to 3%) suggest proceeding, while intervals crossing zero (-2% to 3%) indicate more testing needed
- Eppo platform automates confidence interval calculations, reduces variance through CUPED, and enables real-time monitoring of experiment results
It's one thing to come up with results in any experiment, but it’s a completely different story knowing how confident you can be in those results when it comes to making potentially big decisions for your organization. The 95% confidence interval is a statistical tool that provides a range of values within which the true effect of an experiment is likely to fall, which helps teams evaluate the reliability of their findings.
However, misunderstandings about what the 95% confidence interval does for your experiment can create uncertainty in important decision-making moments. Eppo simplifies calculating, visualizing, and applying confidence intervals so your team can trust the results every time.
In this blog, you’ll learn:
- Why the 95% confidence interval is the go-to choice for experimentation.
- How confidence intervals, sample size, and statistical significance all come together.
- How Eppo helps you use confidence intervals to make smarter, more informed decisions.
What Is a 95% Confidence Interval?
When you run an experiment, your goal is to estimate something about an entire population based on sample data. The 95% confidence interval helps you understand how confident you can be that the true population value lies within a specific range of possible values. If you were to run the same experiment 100 times, the true population value would fall within that range 95 times.
But, it’s important to remember that the 95% confidence interval doesn’t guarantee that the population's true mean is within the interval defined by the sample mean.
It simply reflects the degree of confidence of the estimate based on the random sample you've collected. Even with a 95% confidence level, there’s still a 5% chance the true population mean falls outside that interval because we're dealing with probability, not certainty.
Why the 95% Confidence Interval Is the Standard
In experimentation, you can choose from different confidence intervals, depending on how certain you want to be about your results. The most common is the 95% confidence interval, which gives you a good balance between precision and reliability. But you might also see 90% or 99% confidence intervals used in specific contexts.
The 95% confidence interval became standard in the early 20th century thanks to a statistician named Sir Ronald Fisher, who promoted it as a practical choice for hypothesis testing because it struck a good balance between being reliable enough for most experiments and manageable for everyday use.
The 95% confidence interval is still widely used today because it helps balance false positives (incorrectly finding an effect) and false negatives (missing a real effect). And with 95%, you’re confident enough in your results without being overly strict which can cause inefficiencies in the process. Having a standard is also super helpful because it’s commonly used across many fields which makes it easier to compare results and communicate findings.
Factors That Impact Confidence Intervals
Sample Size
One of the most significant factors that impact the width of your confidence interval is the sample size. Large samples give you more data to work with, which leads to narrower confidence intervals. This means you get a more precise estimate of the true population parameter. On the other hand, small sample sizes result in wider intervals, meaning more uncertainty and less precision. It’s essential to interpret the results carefully with small samples, as they’re more likely to be influenced by random variation.
Variance and Standard Deviation
The variance or standard deviation in your data also plays a significant role in the width of your confidence interval. If your data points are spread out widely (high variance), your confidence interval will be wider, reflecting the greater uncertainty in estimating the true value. Eppo helps manage this by using advanced techniques like CUPED (Controlled Pre-Exposure Data), which reduces variance and standard errors, making the intervals narrower and your results more reliable, especially with smaller sample sizes.
Confidence Level Settings
The confidence level you choose also affects the interval. A higher confidence level, like 99%, will result in a wider confidence interval because you’re allowing more room for the true value to fall within the range. Conversely, a lower level of confidence, like 90%, gives you a narrower interval but with a higher risk that the true value could fall outside the range. The trade-off between confidence and precision is something you’ll want to consider based on the specific needs of your experiment.
Analysis Methods
The statistical methods and experimentation frameworks you use can also affect the width of the confidence interval. Different tests, such as t-tests, z-tests, or using the t-distribution, come with their own assumptions and can produce different results. For example, t-tests are often used when you have smaller sample sizes, and they can result in wider confidence intervals to account for more variability.
How to Use the 95% Confidence Interval in Decision-Making
When using a 95% confidence interval to make decisions about things like feature rollouts, you’ll want to pay special attention to the range of values within the upper and lower bounds of the distribution where you’re likely (95% confident) to find the true population value that you’re trying to estimate.
If the entire interval is above zero (e.g., 1% to 3%), it means the feature is likely to have a positive impact. This is a green light to move forward.
But if the interval crosses zero (e.g., -2% to 3%), the feature could have no effect or even cause a negative one. In that case, more testing is needed before deciding.
Here Are Just a Couple More Best Practices to Keep in Mind
Don’t adjust intervals after seeing the results. Changing the confidence level or adjusting the interval to fit the desired outcome can introduce bias and invalidate the experiment.
Match the confidence interval to the risk profile and goals of your experiment. If a quick decision is needed and the stakes are lower, a 90% confidence level might be sufficient. A 99% confidence level might be more appropriate for high-risk decisions to reduce uncertainty.
Are There Times When a 90% or 99% Interval is a Better Option?
While the 95% confidence interval is a reliable default for most experiments, it might not be the best choice in some situations. Here are some scenarios where adjusting your confidence interval might make sense:
High-Risk Environments
In high-stakes fields like healthcare or finance, a 99% confidence level is often preferred over 95%. The reason? It reduces the risk of false positives—mistakenly concluding that an effect exists when it doesn't. When decisions have significant consequences, being extra sure is far more important than being extra efficient, even if it means having a wider confidence interval. A 99% confidence level provides more certainty that the result is significant and not random.
Early-Stage Experimentation
On the flip side, a 90% confidence level might be more practical in fast-moving or early-stage experimentation. Early experiments often deal with smaller sample sizes, and the goal is to gather quick insights. A 90% confidence level gives you narrower intervals, speeding up decision-making, but it comes with the trade-off of higher false negatives—the risk of missing a true effect. This can be preferable for some when speed is more important than extreme precision.
Eppo Helps You Simplify Confidence Intervals For Better Experimentation
Eppo takes the guesswork out of statistical analysis by automatically calculating confidence intervals, standard errors, and other important metrics. This ensures that your results are both consistent and accurate, saving you time and reducing the chance of errors in manual calculations.
Variance Reduction
One of the challenges with smaller sample sizes is increased variance, which can lead to wider confidence intervals. Eppo addresses this with powerful tools like CUPED and winsorization, which help reduce variance and standard errors. This results in narrower confidence intervals, even when you’re working with limited data.
Sequential Analysis
With Eppo’s sequential analysis, you can continuously monitor experiment results without losing statistical rigor. This means you can make decisions based on real-time data while maintaining your confidence intervals' reliability.
Dashboards for Actionable Insights
Eppo helps you turn complex statistical insights into actionable steps by bringing all your data into one intuitive view. Eppo’s dashboards provide clear, visual representations of confidence intervals, p-values, and other key metrics, so it’s easy to interpret results and make faster, more informed decisions.
Ready to make confidence intervals a key part of your experimentation workflow? Request a demo of Eppo’s platform and see how it can help you streamline your statistical analysis and improve the impact of your experiments.
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